Dynamic rebroadcast scheduling of videos

ABSTRACT

Television viewers want to watch previously broadcast videos for a number of reasons. For example, a viewer may have missed one or more episodes of his or her favorite series. As another example, a viewer may have mistaken the broadcast date of a show. Functionality can be implemented in a video recording device to submit rebroadcast requests for previously broadcast videos to a content provider. The content provider can use the rebroadcast requests to determine popularity of the previously broadcast video and dynamically schedule rebroadcasts of the most popular videos. The rebroadcast requests represent intended viewership of the video and can be leveraged by the content provider when assigning advertisement rates for the rebroadcast.

BACKGROUND

Embodiments of the inventive subject matter generally relate to thefield of video recording, and, more particularly, to dynamic rebroadcastscheduling of videos.

A digital video recorder (DVR) (a.k.a. personal video recorder or PVR)is a device that records audio and video content in a digital format toa disk drive or other medium. DVRs include stand-alone set-top boxes andsoftware for personal computers, where the software enables contentcapture and playback to and from disk. DVRs provide convenient “timeshifting” and other features, such as pausing live TV, instant replay ofscenes, chasing playback, skipping advertising, etc. Most DVRs use aMotion Pictures Expert Group format for encoding video signals.

Many television programs are presented as a series of related episodesthat are intended to be viewed sequentially. A viewer may want to beginwatching a television series, but has missed some of the episodes anddoes not want to wait for several months for the series to be releasedon DVD. A viewer may also want to watch a previously viewed episodeagain.

SUMMARY

Embodiments include a method directed to determining the popularity of apreviously broadcast video based on a number of requests to rebroadcastthe video. It is determined if the popularity of the previouslybroadcast video exceeds a popularity threshold. A rebroadcast of thepreviously broadcast video is dynamically scheduled for a time slot on aday that corresponds with the popularity of the previously broadcastvideo. Advertisements rates for the rebroadcast of the previouslybroadcast video are published.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments may be better understood, and numerous objects,features, and advantages made apparent to those skilled in the art byreferencing the accompanying drawings.

FIG. 1 illustrates an example content delivery system 100.

FIG. 2 depicts a flowchart of example operations to acquire previousepisodes.

FIG. 3 depicts a flowchart of example operations to acquire previousepisodes of a video series.

FIG. 4 depicts an example of a dependency matrix for a video series.

FIG. 5 is flowchart depicting example operations for acquiring previousvideo episodes of a series of video episodes by recording ordownloading.

FIG. 6 is a flowchart depicting example operations for dynamicallyscheduling a rebroadcast of a previously viewed video.

FIG. 7 is an example depiction of the use of rebroadcast requests by acontent provider.

FIG. 8 is a flowchart depicting example operations for dynamicallyscheduling broadcasts and setting advertisement rates.

FIG. 9 depicts an example computer system.

DESCRIPTION OF EMBODIMENT(S)

The description that follows includes exemplary systems, methods,techniques, instruction sequences and computer program products thatembody techniques of the present inventive subject matter. However, itis understood that the described embodiments may be practiced withoutthese specific details. For instance, although examples refer to digitalvideo recorders and personal video recorders, embodiments can beimplemented with a video game console, a portable video recordingdevice, a computer, etc. In other instances, well-known instructioninstances, protocols, structures and techniques have not been shown indetail in order not to obfuscate the description.

Viewing episodes of a video series in order allows for a good viewingexperience and understanding of episode content of the individualepisodes. Functionality can be implemented in a video recording deviceand/or at a content provider to collect data about viewing behavior todetermine if a user(s) tends to view episodes of a series in order. Thevideo recording device and/or content provider can also keep track ofpartially or fully viewed episodes and episodes that are ready forviewing to avoid acquiring already viewed episodes. Being able toquickly catch up on missed episodes will allow for easier introductionto a video series and prevent viewers from abandoning programs. Inaddition, requests for particular episodes can be leveraged for dynamicepisode scheduling and dynamic setting of advertisement rates.

FIG. 1 illustrates an example content delivery system 100. The contentdelivery system includes a digital video recorder/personal videorecorder (DVR/PVR) unit 101, display device 112 (e.g., television,monitor, projector, etc.), network 109, content provider 110, and videoepisode dependency service 113. The content provider 110 can providetelevision content via a cable television infrastructure (e.g., opticalfiber, coaxial cables, etc.) or other infrastructures, such as digitalsubscriber lines (DSL). The DVR unit 101 includes a storage device 102,video episode detection unit 104, and previous episode acquisition unit108, all of which are connected via a bus 103. Although FIG. 1 shows theDVR's components connected via a bus 103, the components can beconnected using other technologies (e.g., software interfaces). Thestorage device 102 hosts video episodes 106, episode dependencyinformation 105, and viewing behavior data 107. Although FIG. 1 showsthe episode dependency information being stored locally, the episodedependency information may be stored remotely, previously retrieved froma remote source, refreshed from a remote source, etc. In thisillustration, the DVR/PVR unit 101 accesses the video episode dependencyservice 113 to determine dependencies. For instance, the DVR/PVR unit101 accesses the video episode dependency service 113 to determine if aselected episode 7 being broadcast or scheduled to be broadcast isdependent on information provided in one or more previous episodes. TheDVR/PVR 101 can retrieve this information from the video episodedependency service 113 for plural video series or for the correspondingvideo series (e.g., the DVR/PVR unit 101 can fetch dependencyinformation for only the corresponding video series or for multiplevideo series that include the corresponding video series as ifprefetching dependency information for other video series). The DVR/PVRunit 101 stores the dependency information in the storage device 102.The DVR/PVR unit 101 can add the dependency information from the service113 to the episode dependency information 105, partially overwrite theepisode dependency information 105, or entirely overwrite the episodedependency information 105.

The video episode detection unit 104 detects selection of a video anddetermines if the selected video is an episode in a video series.Example techniques for determining if the selected video is an episodein a series include matching the main title of the selected video topreviously aired programs, checking for an episode number and/or episodetitle, etc. Titles, episode numbers and other program information for avideo can be obtained from an EPG, metadata associated with videoepisodes, data accessed over a network, and/or a third party cableservice.

It is determined which, if any, video episodes in a series are dependenton previous episodes. Episodes are dependent on one another if a viewershould watch a previous episode in order to fully understand the contentof a subsequent episode (e.g., television drama series, televisionmini-series, etc.). An episode can be dependent on all of the previousepisodes, a subset of the previous episodes, or none of the previousepisodes. If the selected video is an episode in a series, the videoepisode detection unit 104 requests episode dependency information fromthe video episode dependency service 113. The episode dependencyinformation service 113 may be part of the EPG, data accessed over anetwork, and/or a third party cable service.

Once the episode dependencies are determined, the previous video episodeacquisition unit 108 acquires any previous video episodes that have notalready been watched or stored in memory. Viewing behavior data 107 isused to determine whether a user(s) associated with the DVR/PVR unittends to view episodes of a series in order. If a user indicates (e.g.,sets preference) or viewing behavior suggests that a user tends not toadhere to series order, then the DVR/PVR can prompt the user beforeacquiring an episode or simply not acquire the episode. The videoepisodes data 106 comprises recorded video episodes and indications ofother information about previously viewed episodes. If an episode hasbeen viewed or is already stored in the device, that episode will not bedownloaded. The viewing behavior data 107 could be stored locally on theDVR and/or on a backend server at the content provider. In addition, theviewing behavior data 107 and the video episodes data 106 can berepresented with a same set of data. For example, the DVR/PVR unit 101can examine episodes that are currently stored to determine if theuser(s) watch episodes without regard to order. In another example, theDVR/PVR unit 101 maintains a counter for dependent episodes in a videoseries watched without the benefit of the one or more previous episodes.In another example, the DVR/PVR unit 101 maintains a flag, perhaps foreach video series, that indicates whether a user(s) has watched adependent episode of a series without watching a previous episode.

Embodiments do not necessarily retrieve episodes automatically. ADVR/PVR unit can inform a user of dependencies and suggest a particularviewing order. For example, embodiments can display a graphical userinterface that indicates selected episode 8 is dependent on episodes 7and 5 and suggests viewing episodes 5, 7, and 8 in order. The examplegraphical user interface can prompt the user to direct the unit to fetchthe episodes 7 and 5, select which of the previous episodes to retrieve,or to ignore the dependencies and view episode 8. Embodiments can alsoprevent viewing of an episode that is dependent upon a previous unviewedepisode (e.g., not allow a user to view episode 8 until episodes 5 and 7have been viewed, or at least retrieved).

Although not shown in FIG. 1, the DVR unit 101 includes components forrecording and presenting content (e.g., video decoding logic, read/writelogic, video tuner(s), etc.). Furthermore, any of the components shownherein can include hardware, firmware, and/or machine-readable mediaincluding instructions for performing the operations described herein.

FIG. 2 depicts a flowchart of example operations to acquire previousepisodes. Flow 200 begins at block 201, where selection of a video thatis part of a video series is detected. Examples of detecting selectionof a video include detecting selection of a video for live televisionviewing, detecting changing live television channels, detectingscheduling of a new recording of a video, etc. Data from various sources(e.g., the EPG, metadata, etc.) indicates the video is part of a series.

At block 203, episode dependency information and viewing behavior areexamined to determine which previous episodes to acquire. Dependencyinformation could be acquired from the EPG, and/or a third partyservice. An example of a third party service includes a service thataccumulates episode dependency information from users. For instance, acommunity of users tag episodes or provide commentary that identifiesdependencies among episodes of a series. Another example of a thirdparty service includes a content provider pushing dependency informationdown to DVRs. An episode could be dependent upon all of the previousepisodes or upon a portion of a single previous episode. Viewingbehavior data is used to determine if there is a preference and/ortendency to view episodes of video series in order. Information aboutpreviously recorded content is also exampled to determine which previousepisodes, if any, have already been viewed or acquired.

At block 205, the EPG and other download services are searched foravailability of previous episodes. If a rebroadcast of a previousepisode is indicated in the EPG, the DVR will schedule a recording ofthe rebroadcast. If the previous episode is available for download froma web service, the DVR will initiate a download of the previous episode.A user can set various configurations for acquiring a video. Forexample, configurations can indicate whether recording or downloading ispreferred. Configurations can also indicate whether a user is willing topay for acquisition of an episode, and an acceptable price range(s). Atblock 207, previous episodes are recorded or downloaded.

FIG. 3 depicts a flowchart of example operations to acquire previousepisodes of a video series. Flow 300 begins at block 301, whereselection of a video from a plurality of video content is detected.Examples of detecting selection of a video include detecting selectionof a video for live television viewing, detecting changing livetelevision channels, detecting scheduling of a new recording of a video,etc. At block 303, it is determined if the selected video is an episodein a video series. As an example, a DVR examines an electronicprogramming guide (EPG). As another example, the DVR accesses a videoepisode dependency service, which supplies data that indicatesdependencies between episodes in a series. If the selected video is notpart of a video series, the flow ends. If the selected video is part ofa video series, then the flow continues to block 305.

At block 305, it is determined if viewing behavior data indicates apreference to view episodes of a series in order. Example techniques todetermine if a viewing behavior data indicates a preference to viewvideo series episodes in order include prompting a user to determine ifhe/she wants to watch the episodes in order, examining historical datathat indicates prior viewing behavior of the video series (or othervideo series) to check if other episodes have been viewed out of order,etc. If it is determined that viewing behavior data does not indicate apreference to view episodes of a series in order, then the flow ends. Ifviewing behavior indicates that there is a preference to view theepisodes of the series in order, the flow continues at block 307.

At block 307, it is determined if previous video episodes in the serieshave been viewed. To determine if a video episode has been viewed, atracking mechanism (e.g., a database) is used to store prior viewinghistory. This viewing history data could be stored locally on the DVRand/or on a backend server at the content provider. If previous videoepisodes in the series have been viewed, the flow ends. If previousepisodes have not been viewed, flow continues at block 309. At block309, unviewed previous video episodes are retrieved and the flow ends.For example, these previous video episodes can be obtained byautomatically scheduling recording of a rebroadcast. In another example,the previous episodes could be downloaded from a web service.

In some cases, it may not be necessary to acquire all of the previousepisodes of a series. For example, episode 3 may be dependent on episode1, but not episode 2 because episode 2 does not contain informationrelevant to the content (e.g., storyline) of episode 3. Therefore, onlyone of the two previous episodes will be acquired.

FIG. 4 depicts an example of a dependency matrix for a video series. Atstage A, a video episode detection unit 403 examines a selection 401 ofa video episode of a video series, and detects that episode 7 of thevideo series has been selected. At stage B, the video episode detectionunit 403 accesses episode dependency data 405. The episode dependencydata 405 can be obtained from the EPG, metadata associated with videoepisodes, data accessed over a network, and/or a third party cableservice. The dependency data 405 can be every episode in the series thataired before the current selected episode or a subset of the previousepisodes that are related to the content of the selected episode. Atstage C, a previous video episode acquisition unit 407 determines whichprevious episodes the selected episode is dependent upon, and builds adependency chain for episode 7. For example, the previous video episodeacquisition unit 407 creates a structure that represents direct andindirect dependencies of episodes from episode 7 while walking thedependency data 405. In some cases, an episode may have more than onedependency chain. As an example, an episode can be dependent upon all ofthe regular previous episodes of the series or upon a special overviewepisode. The special overview episode recaps important storylineinformation from all of the regular previous episodes. The dependencychain indicating the special overview episode is more compact than thechain indicating all regular previous episodes. A user can setconfigurations with regard to preferred dependency chains. Examplesinclude a user indicating a preference for compact dependency chains, auser indicating a preference for overview episodes, a user indicating apreference for minimal viewing time, etc. A unit or module (e.g.,program product or application specific integrated circuit) canautomatically request episodes and/or schedule recordings based onvarious parameters (e.g., total running time of videos in a dependencychain, number of nodes in a dependency chain, etc.). Dependency chainconfigurations can be applied differently to one or more series or thesame to all series. Furthermore, embodiments can also utilize valuesalong with indications of dependencies to indicate a particular qualityof a video episode. For example, the special overview episode thatrecaps a previous season can be associated with a value that identifiesit as a recap of the previous season. Embodiments can also associateepisodes with values that represent priority and/or relevancy of theepisodes. For example, the special overview episode can be given ahigher value that suggests greater priority. The individual episodes ofthe previous season can also be associated with various values thatrepresent their level of relevancy. Although a subject episode may bedependent upon eight episodes of the previous season, some of thoseprevious episodes may only have marginal relevance with respect to acharacter who is killed off.

At stage D, the previous video episode acquisition unit 407 acquires theprevious episodes based on the dependency chain and prior viewinghistory. Episodes that have not yet been viewed or recorded are acquiredby the video acquisition unit 407.

FIG. 5 is a flowchart depicting example operations for acquiringprevious video episodes of a series of video episodes by recording ordownloading. Flow 500 begins at block 501, where it is determined if aprevious episode can be recorded from a rebroadcast of that episode. Forexample, a searching mechanism (e.g., implemented in the DVR and/orback-end service) searches an electronic programming guide for arebroadcast of the previous episode. The electronic programming guidemay not indicate a rebroadcast and/or the searching mechanism may limitsearching within a given time period. If the previous episode can berecorded, then the flow continues at block 503. At block 503, the DVRschedules recording of the previous episode to be rebroadcast. Flow thencontinues at block 511.

If a rebroadcast of the previous episode is not or cannot be scheduledfor recording, then flow continues at block 505. At block 505, it isdetermined if the episode can be downloaded from a web service. If theepisode can be downloaded from a web service, flow continues to block507. If the episode cannot be downloaded, flow continues to block 509.

At block 507, the previous episode is downloaded. Control flows fromblock 507 to block 511.

At block 509, a rebroadcast request is sent to the content provider.

At block 511, it is determined if there are more episodes to acquire.For example, episode 4 may depend on information from both episodes 1and 3. As another example, episode 4 may depend on information inepisode 3, which in turn depends on information in episode 2. If thereare more episodes to acquire, flow returns to block 501. If there are nomore episodes to acquire, the flow ends.

In block 509, a rebroadcast request is sent to the content provider whenan episode cannot be recorded or downloaded. The content provider canuse these requests to schedule re-runs of more popular episodes in aseries. The number of requests for a specific episode can then be usedby the content provider to determine advertising rates for therebroadcast. Episodes that are more popular (i.e., those episodes with alarge number of rebroadcast requests) can be scheduled for rebroadcastat prime times and with larger advertisement rates.

Television viewers want to watch previously broadcast videos for anumber of reasons. For example, a viewer may have missed one or moreepisodes of his or her favorite series. As another example, a viewer mayhave mistaken the broadcast date of a show. Functionality can beimplemented in a video recording device to submit rebroadcast requestsfor previously broadcast videos to a content provider. The contentprovider can use the rebroadcast requests to determine popularity of thepreviously broadcast video and dynamically schedule rebroadcasts of themost popular videos. The rebroadcast requests represent intendedviewership of the video and can be leveraged by the content providerwhen assigning advertisement rates for the rebroadcast.

FIG. 6 is a flowchart depicting example operations for dynamicallyscheduling a rebroadcast of a previously viewed video. Flow starts atblock 601, where the popularity of a previously broadcast video isdetermined. The popularity is based, at least in part, on the numberrequests to rebroadcast the video. Embodiments can use varioustechniques to derive popularity from number of rebroadcast requests. Forinstance, embodiments can use a raw number of requests as an indicationof popularity. In another embodiment, a content provider and/or thirdparty service provide collect demographic data and apply the demographicdata to the requests to weight requests or modify the numbers.

At block 603, it is determined if the popularity of the previouslybroadcast video exceeds a popularity threshold. The popularity thresholdis set by the content provider as a value (e.g., the number of requests,perhaps adjusted by demographic data) for the video to be eligible forrebroadcast.

At block 604, the previously broadcast video is dynamically scheduledfor rebroadcast during a time slot on a day that corresponds to thepopularity of the video. The video may be rebroadcast on a specialrebroadcast channel, during breaks in broadcasting of new episodes in aseries, etc. In embodiments, the time slot is chosen by examining thenumber of rebroadcast requests and/or the popularity threshold.

At block 605, advertisement rates for the previously broadcast video arepublished based, at least in part, on the time slot. The advertisementrates correspond to the chosen time slot for the rebroadcast.

Although numerous examples refer to a series, embodiments can requestrebroadcast of videos that are not a part of a series. A user can searchan EPG for a past broadcast of a standalone video (e.g., documentary, TVmovie, debate, etc.). The user can then request rebroadcast of thestandalone video.

FIG. 7 is an example depiction of the use of broadcast requests by acontent provider. A content provider receives requests 701 for broadcastof specific episodes. A table 703 indicates the requested episodes andcorresponding number of broadcast requests for each episode. In thisillustration, the table 703 indicates video episodes X, Y, and Z thathave respectively accumulated 250,899 broadcast requests, 1000 broadcastrequests, and 4899 broadcast requests. At stage A, a dynamic broadcastscheduling unit 705 determines the number of requests for a specificvideo episode. A table 707 is an example structure that associatespopularity level as represented by ranges of broadcast requests,broadcast time slots, and advertisement rates for 30 second spots withineach time slot. At stage B, the dynamic broadcast scheduling unit 705schedules video episodes in time slots based on popularity level.

At stage C, a rate adjustment unit 709 selects the specific broadcasttime slot for the video episode X within the time slot and publishes theadvertising rate associated with that time slot. As an example, the rateadjustment unit 709 schedules a broadcast of an episode X at 5:00 p.m.In the table 707, the corresponding advertisement rate for a 30 secondspot for broadcasts between 5:00 p.m. and 6:59 p.m. is $3000. The rateadjustment unit will publish a rate of $3000 for a 30 secondadvertisement during the broadcast of episode X.

At stage D, the rate adjustment unit 709 augments the rate if certaincriteria are met. The rate may be augmented for a number of reasons.There may be more episodes that meet the popularity conditions for aspecific time slot than can actually be aired within that time slot onany specific day. The content provider can handle this situation invarious manners: decide to air episodes that do not fit into thespecific day's time slot in the same time slot on another day; air theepisodes in a different time slot the same day and augment the base ratefor the chosen slot; etc. The content provider may also decide to acceptbids for the most popular time slots based on, or completely independentof, the established rates. The content provider may utilize additionalparameters to augment advertisement rates. For instance, there may becertain days of the week that are more popular for viewing than others.The content provider may choose to augment the base time slot rate basedon more popular viewing days. In this example, broadcast requests forprevious episodes of a video series automatically are sent to thecontent provider. In another example, requests for any previously airedvideo may be manually sent to the content provider. Embodiments can usevarious techniques allowing requests to be submitted and/or handled.Examples of techniques include encoding functionality within anelectronic programming guide to submit requests for a video, providingan Internet browser to both search for videos and submit requests forthe videos, etc. A user can submit requests for previously aired contentor for content (e.g., a movie) that has never been aired.

Embodiments can broadcast the requested episodes on one or more channelsavailable for general viewing as well as DVR recording. However, thecontent provider may choose to broadcast requested videos on a channelonly accessible with a video recording device (e.g., DVR, game console,media center, etc.). The content provider can limit advertisements onthe broadcast channel. For example, the content provider can requireviewing of an advertisement before viewing the video and/or insert oneor more advertisements at a halfway point in the video being broadcast.In addition, the content provided can encode the broadcast to preventfast-forwarding and/or skipping.

FIG. 8 is a flowchart depicting example operations for dynamicallyscheduling broadcasts and setting advertisement rates. Flow begins atblock 801, where a content provider receives broadcast requests for aspecific video. The requested video could be any previously airedcontent or new content that has not been aired.

At block 803, it is determined if the popularity threshold has beenreached. To illustrate, a content provider can set the popularitythreshold as a number of requests received for a specific video to beeligible for broadcast. If the popularity threshold has been met, thenflow continues at block 805. If the popularity threshold has not beenmet, the flow ends.

At block 805, the video is scheduled for broadcast. Scheduling is inaccordance with various parameters. For example, broadcast times arepicked based on popularity as represented by the number of rebroadcastrequests. At block 807, an advertisement rate for a spot with respect topopularity of the video is determined.

At block 809, it is determined if the advertisement rate should beaugmented. The advertisement rate may be augmented based on a variety ofparameters. Example parameters include more videos are eligible for aspecific time slot than can be accommodated, the video is scheduled tobe aired on a more popular day, advertisement space within the broadcastis filling up, etc. If the rate should be augmented, flow continues atblock 811. If the rate should not be augmented, the flow ends.

At block 811, the new advertisement rate is published and the flow ends.

It should be understood that the depicted flowcharts are examples meantto aid in understanding embodiments and should not be used to limitembodiments or limit scope of the claims. Embodiments may performadditional operations, fewer operations, operations in a differentorder, operations in parallel, and some operations differently. Forinstance, operations in FIG. 3 for determining which previous episodeshave been watched and determining to watch episodes in order could beinterchanged. Referring to FIG. 4, a mechanism to track the subset ofprevious episodes that are helpful in viewing the selected episode maynot be implemented. In this case, the selected episode would beconsidered to be dependent on all previous episodes.

Embodiments may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.” Furthermore, embodiments of the inventive subjectmatter may take the form of a computer program product embodied in anytangible medium of expression having computer usable program codeembodied in the medium. The described embodiments may be provided as acomputer program product, or software, that may include amachine-readable medium having stored thereon instructions, which may beused to program a computer system (or other electronic device(s)) toperform a process according to embodiments, whether presently describedor not, since every conceivable variation is not enumerated herein. Amachine readable medium includes any mechanism for storing ortransmitting information in a form (e.g., software, processingapplication) readable by a machine (e.g., a computer). Themachine-readable medium may include, but is not limited to, magneticstorage medium (e.g., floppy diskette); optical storage medium (e.g.,CD-ROM); magneto-optical storage medium; read only memory (ROM); randomaccess memory (RAM); erasable programmable memory (e.g., EPROM andEEPROM); flash memory; or other types of medium suitable for storingelectronic instructions. In addition, embodiments may be embodied in anelectrical, optical, acoustical or other form of propagated signal(e.g., carrier waves, infrared signals, digital signals, etc.), orwireline, wireless, or other communications medium.

Computer program code for carrying out operations of the embodiments maybe written in any combination of one or more programming languages,including an object oriented programming language such as Java,Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on a user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN), a personal area network(PAN), or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider).

FIG. 9 depicts an example computer system. A computer system includes aprocessor unit 901 (possibly including multiple processors, multiplecores, multiple nodes, and/or implementing multi-threading, etc.). Thecomputer system includes memory 907. The memory 907 may be system memory(e.g., one or more of cache, SRAM, DRAM, zero capacitor RAM, TwinTransistor RAM, eDRAM, EDO RAM, DDR RAM, EEPROM, NRAM, RRAM, SONOS,PRAM, etc.) or any one or more of the above already described possiblerealizations of machine-readable media. The computer system alsoincludes a bus 903 (e.g., PCI, ISA, PCI-Express, HyperTransport®,InfiniBand®, NuBus, etc.), a network interface 909 (e.g., an ATMinterface, an Ethernet interface, a Frame Relay interface, SONETinterface, wireless interface, etc.), an episode order adherence unit921, and a storage device(s) 911 (e.g., optical storage, magneticstorage, etc.). The dynamic rebroadcast scheduling unit 921 determinespopularity of a previously broadcast video, schedules rebroadcast of thevideo, and publishes advertising rates for the rebroadcast. Some or allof the functionality of the dynamic rebroadcast scheduling unit 921 maybe implemented with code embodied in memory and/or a processor,co-processors, other cards, etc. Any one of these functionalities may bepartially (or entirely) implemented in hardware and/or on the processingunit 901. For example, the functionality may be implemented with anapplication specific integrated circuit, in logic implemented in theprocessing unit 901, in a co-processor on a peripheral device or card,etc. Further, realizations may include fewer or additional componentsnot illustrated in FIG. 9 (e.g., video cards, audio cards, additionalnetwork interfaces, peripheral devices, etc.). The processor unit 901,the storage device(s) 911, the dynamic rebroadcast scheduling unit 921,and the network interface 909 are coupled to the bus 903. Althoughillustrated as being coupled to the bus 903, the memory 907 may becoupled to the processor unit 901.

While the embodiments are described with reference to variousimplementations and exploitations, it will be understood that theseembodiments are illustrative and that the scope of the inventive subjectmatter is not limited to them. In general, techniques described hereinmay be implemented with facilities consistent with any hardware systemor hardware systems. Many variations, modifications, additions, andimprovements are possible.

Plural instances may be provided for components, operations orstructures described herein as a single instance. Finally, boundariesbetween various components, operations and data stores are somewhatarbitrary, and particular operations are illustrated in the context ofspecific illustrative configurations. Other allocations of functionalityare envisioned and may fall within the scope of the inventive subjectmatter. In general, structures and functionality presented as separatecomponents in the exemplary configurations may be implemented as acombined structure or component. Similarly, structures and functionalitypresented as a single component may be implemented as separatecomponents. These and other variations, modifications, additions, andimprovements may fall within the scope of the inventive subject matter.

1. A method comprising: determining popularity of a previously broadcastvideo based, at least in part, on a number of requests to rebroadcastthe previously broadcast video; determining if the popularity of thepreviously broadcast video exceeds a popularity threshold; dynamicallyscheduling the previously broadcast video for rebroadcast at a time sloton a day that corresponds with the popularity of the previouslybroadcast video; and publishing one or more advertisement rates for thepreviously broadcast video based, at least in part, on the time slot. 2.The method of claim 1 further comprising dynamically selecting a channelfor rebroadcast of the previously broadcast video based, at least inpart, on the popularity.
 3. The method of claim 1 further comprising:determining that popularity of a second previously broadcast video alsoexceeds the popularity threshold; determining that the popularity of thesecond previously broadcast video is less than the popularity of thepreviously broadcast video; dynamically scheduling the second previouslybroadcast video for rebroadcast at a second time slot; modifying adefault advertisement rate for the second time slot; and publishing themodified default advertisement rate for the second previously broadcastvideo.
 4. The method of claim 1 further comprising: determining thatpopularity of a second previously broadcast video also exceeds thepopularity threshold; determining that the popularity of the secondpreviously broadcast video is less than the popularity of the previouslybroadcast video; dynamically scheduling the second previously broadcastvideo for rebroadcast at the time slot on a different day than therebroadcast of the previously broadcast video.
 5. The method of claim 1further comprising: determining that popularity of a second previouslybroadcast video also exceeds the popularity threshold; determining thatthe popularity of the second previously broadcast video is less than thepopularity of the previously broadcast video; dynamically scheduling thesecond previously broadcast video for rebroadcast at the time slot on adifferent channel than the rebroadcast of the previously broadcastvideo.
 6. The method of claim 1 further comprising modifying a set ofone or more default advertisement rates for the time slot based, atleast in part, on the popularity of the previously broadcast video,wherein the published one or more rates comprise the modified set of oneor more default advertisement rates.
 7. The method of claim 1, whereinthe one or more advertisement rates are also based on at least one ofthe day of the rebroadcast and the channel of the rebroadcast.
 8. Themethod of claim 1, wherein said determining if the popularity of thepreviously broadcast video exceeds a popularity threshold comprises:determining which of a plurality of popularity ranges encompasses thepopularity of the previously broadcast video.
 9. The method of claim 1further comprising conducting an auction for one or more advertisementspots associated with the rebroadcast, wherein said one or moreadvertisement rates are based, at least in part, on the auction.
 10. Oneor more machine-readable media having stored therein a program product,which when executed a set of one or more processor units causes the setof one or more processor units to perform operations that comprise:determining popularity of a previously broadcast video based, at leastin part, on a number of requests to rebroadcast the previously broadcastvideo; determining if the popularity of the previously broadcast videoexceeds a popularity threshold; dynamically scheduling the previouslybroadcast video for rebroadcast at a time slot on a day that correspondswith the popularity of the previously broadcast video; and publishingone or more advertisement rates for the previously broadcast videobased, at least in part, on the time slot.
 11. The machine-readablemedia of claim 10, wherein the operations further comprise dynamicallyselecting a channel for rebroadcast of the previously broadcast videobased, at least in part, on the popularity.
 12. The machine-readablemedia of claim 10, wherein the operations further comprise: determiningthat popularity of a second previously broadcast video also exceeds thepopularity threshold; determining that the popularity of the secondpreviously broadcast video is less than the popularity of the previouslybroadcast video; dynamically scheduling the second previously broadcastvideo for rebroadcast at a second time slot; modifying a defaultadvertisement rate for the second time slot; and publishing the modifieddefault advertisement rate for the second previously broadcast video.13. The machine-readable media of claim 10, wherein the operationsfurther comprise: determining that popularity of a second previouslybroadcast video also exceeds the popularity threshold; determining thatthe popularity of the second previously broadcast video is less than thepopularity of the previously broadcast video; dynamically scheduling thesecond previously broadcast video for rebroadcast at the time slot on adifferent day than the rebroadcast of the previously broadcast video.14. The machine-readable media of claim 10, wherein the operationsfurther comprise: determining that popularity of a second previouslybroadcast video also exceeds the popularity threshold; determining thatthe popularity of the second previously broadcast video is less than thepopularity of the previously broadcast video; dynamically scheduling thesecond previously broadcast video for rebroadcast at the time slot on adifferent channel than the rebroadcast of the previously broadcastvideo.
 15. The machine-readable media of claim 10, wherein theoperations further comprise modifying a set of one or more defaultadvertisement rates for the time slot based, at least in part, on thepopularity of the previously broadcast video, wherein the published oneor more rates comprise the modified set of one or more defaultadvertisement rates.
 16. The machine-readable media of claim 10 whereinthe one or more advertisement rates are also based on at least one ofthe day of the rebroadcast and the channel of the rebroadcast.
 17. Themachine-readable media of claim 10, wherein said operation ofdetermining if the popularity of the previously broadcast video exceedsa popularity threshold comprises: determining which of a plurality ofpopularity ranges encompasses the popularity of the previously broadcastvideo.
 18. An apparatus comprising: a set of one or more processorunits; a storage device operable to store videos; a network interfaceoperable to transmit data and receive data; and one or moremachine-readable media having stored therein a program product, whichwhen executed a set of one or more processor units causes the set of oneor more processor units to perform operations that comprise, determiningpopularity of a previously broadcast video based, at least in part, on anumber of requests to rebroadcast the previously broadcast video;determining if the popularity of the previously broadcast video exceedsa popularity threshold; dynamically scheduling the previously broadcastvideo for rebroadcast at a time slot on a day that corresponds with thepopularity of the previously broadcast video; and publishing one or moreadvertisement rates for the previously broadcast video based, at leastin part, on the time slot.
 19. The apparatus of claim 18 furthercomprising a dynamic rebroadcast scheduling unit that comprises the oneor more machine-readable media.
 20. The apparatus of claim 18 furthercomprising a rate adjustment unit operable to modify a set of one ormore default advertisement rates for the rebroadcast of the previouslybroadcast video based, at least in part, on the popularity of thepreviously broadcast video.